Reductions in perceived COVID‐19 threat amid UK’s mass public vaccination programme coincide with reductions in outgroup avoidance (but not prejudice)
Bibliographic record
Abstract
Abstract It has long been proposed that perceptions of threat contribute to greater outgroup negativity. Much of the existing evidence on the threat–prejudice association in the real world, however, is cross‐sectional in nature. Such designs do not adequately capture individual‐level changes in constructs, and how changes in constructs relate to changes in other theoretically relevant constructs. The current research exploited the unique opportunity afforded by the mass COVID‐19 vaccination programme in the United Kingdom to explore whether reductions in pathogen threat coincide with reductions in outgroup prejudice and avoidance. A two‐wave longitudinal study ( N 1 = 912, N 2 = 738) measured British adult's perceptions of COVID‐19 threat and anti‐immigrant bias before and during mass vaccine rollout in the United Kingdom. Tests of latent change models demonstrated that perceived COVID‐19 threat significantly declined as the vaccine programme progressed, as did measures of outgroup avoidance tendencies, but not prejudiced attitudes. Critically, change in threat was systematically correlated with change in outgroup avoidance: those with greater reductions in perceived COVID‐19 threat were, on average, those with greater reductions in outgroup avoidance. Findings provide important and novel insights into the implications of disease protection strategies for intergroup relations during an actual pandemic context, as it unfolds over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".